Green Card Physical Scientist Jobs
Physical Scientist jobs with green card sponsorship are available through EB-2 and EB-3 pathways, both requiring PERM labor certification filed with the Department of Labor. Employers must document a prevailing wage determination and complete a recruitment test before petitioning USCIS for permanent sponsorship of your position.
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A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
At Genentech (gRED) and Roche (pRED), we’re applying Physical AI to solve one of humanity’s hardest challenges: building fully autonomous, self-healing laboratories for lights-out drug discovery.
Advances in AI, data, and computational sciences are transforming drug discovery and development. The Computational Sciences Center of Excellence (CoE) and the Data and Digital Catalyst (DDC) team are building the foundational platform for autonomous labs – turning state-of-the-art AI into real-world physical action to deliver life-saving medicines to patients faster than ever before.
As a Senior Physical AI & Robotics Scientist, you will be the hands-on technical lead and scientific architect bringing Physical AI to life – deploying 3D perception, multi-physics digital twins, edge vision, and closed-loop control onto state-of-the-art robotic hardware using cutting-edge stacks like NVIDIA Isaac, Omniverse, and Metropolis. If you are inspired by the challenge of bridging foundation models with complex real-world hardware to give robots the spatial awareness and resilience to autonomously solve problems on the lab floor, this is your opportunity to pioneer the next generation of autonomous laboratories at global scale.
In this role, you will:
- Develop 3D computer vision perception pipelines (point clouds, depth maps, pose estimation) and dynamic motion planning algorithms (ROS 2, MoveIt 2, cuMotion) to enable reliable arm manipulation, hand-eye calibration, grasping, and AMR navigation in dynamic workcells.
- Build multi-physics simulation environments (NVIDIA Isaac Sim/Lab, Newton Engine) to model robot-labware interactions, evaluate LLM-generated execution plans inside digital twins before deployment, and train policy recovery models.
- Apply system identification, domain randomization, and sim-to-real tuning to ensure simulation-trained control policies execute predictably on physical hardware.
- Fine-tune multimodal Vision AI models (VLMs, NVIDIA Cosmos) using synthetic failure datasets (Isaac Sim) for real-time detection of lab anomaly modes (spills, bubbles, dropped microplates, tilted lids, tip-pick misses).
- Optimize low-latency inference with TensorRT on NVIDIA Jetson (Thor/Orin) edge hardware, combining visual servoing and closed-loop feedback control so systems self-correct without halting master workflows.
- Harness OpenUSD scene graphs to represent workcell geometry and state, integrating synchronization services to link executable digital twins across heterogeneous hardware APIs (SiLA 2, gRPC, vendor SDKs).
Who You Are
Core Qualifications & Experience
- MS in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related technical field with 5+ years of experience, or a PhD with 2+ years of experience in deploying Physical AI or robotics solutions.
- Strong interest in translating physical AI capabilities into biological workflows and drug discovery pipelines.
- Passionate builder with a proven track record of taking real-world robotic manipulation and sim-to-real systems beyond demonstrations into sustained operation.
- Strong background in 3D computer vision, kinematics, motion planning (ROS 2, MoveIt 2, cuMotion), and modern machine learning frameworks (PyTorch) with strong C++/Python skills.
- Hands-on experience with robotics simulation and robot-learning frameworks (NVIDIA Isaac Sim/Lab, Newton, MuJoCo, Drake, Gazebo) and sim-to-real validation methods.
- Experience training or fine-tuning vision/multimodal models (VLMs, Cosmos) and optimizing neural networks with TensorRT for edge hardware (Jetson Thor/Orin).
- Proficiency with visual servoing, closed-loop controllers, and hardware API integration (gRPC, SiLA 2, vendor SDKs).
Preferred but not required
- Hands-on experience with OpenUSD scene graphs, custom USD schemas, or NVIDIA Omniverse workflows.
- Experience working with high-throughput lab automation hardware, manipulators, or liquid handlers (e.g., Hamilton, Tecan, Precise Automation, UR arms).
- Background in imitation learning, reinforcement learning, or robot foundation models for contact-rich manipulation and recovery.
- A public portfolio of real-world projects on GitHub/GitLab showcasing work in robotics, simulation, or physical AI.
Relocation benefits are NOT available for this opportunity.
The expected salary range for this position based on the primary location of San Francisco is $168,100 - 312,300 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
The Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
At Genentech (gRED) and Roche (pRED), we’re applying Physical AI to solve one of humanity’s hardest challenges: building fully autonomous, self-healing laboratories for lights-out drug discovery.
Advances in AI, data, and computational sciences are transforming drug discovery and development. The Computational Sciences Center of Excellence (CoE) and the Data and Digital Catalyst (DDC) team are building the foundational platform for autonomous labs – turning state-of-the-art AI into real-world physical action to deliver life-saving medicines to patients faster than ever before.
As a Senior Physical AI & Robotics Scientist, you will be the hands-on technical lead and scientific architect bringing Physical AI to life – deploying 3D perception, multi-physics digital twins, edge vision, and closed-loop control onto state-of-the-art robotic hardware using cutting-edge stacks like NVIDIA Isaac, Omniverse, and Metropolis. If you are inspired by the challenge of bridging foundation models with complex real-world hardware to give robots the spatial awareness and resilience to autonomously solve problems on the lab floor, this is your opportunity to pioneer the next generation of autonomous laboratories at global scale.
In this role, you will:
- Develop 3D computer vision perception pipelines (point clouds, depth maps, pose estimation) and dynamic motion planning algorithms (ROS 2, MoveIt 2, cuMotion) to enable reliable arm manipulation, hand-eye calibration, grasping, and AMR navigation in dynamic workcells.
- Build multi-physics simulation environments (NVIDIA Isaac Sim/Lab, Newton Engine) to model robot-labware interactions, evaluate LLM-generated execution plans inside digital twins before deployment, and train policy recovery models.
- Apply system identification, domain randomization, and sim-to-real tuning to ensure simulation-trained control policies execute predictably on physical hardware.
- Fine-tune multimodal Vision AI models (VLMs, NVIDIA Cosmos) using synthetic failure datasets (Isaac Sim) for real-time detection of lab anomaly modes (spills, bubbles, dropped microplates, tilted lids, tip-pick misses).
- Optimize low-latency inference with TensorRT on NVIDIA Jetson (Thor/Orin) edge hardware, combining visual servoing and closed-loop feedback control so systems self-correct without halting master workflows.
- Harness OpenUSD scene graphs to represent workcell geometry and state, integrating synchronization services to link executable digital twins across heterogeneous hardware APIs (SiLA 2, gRPC, vendor SDKs).
Who You Are
Core Qualifications & Experience
- MS in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related technical field with 5+ years of experience, or a PhD with 2+ years of experience in deploying Physical AI or robotics solutions.
- Strong interest in translating physical AI capabilities into biological workflows and drug discovery pipelines.
- Passionate builder with a proven track record of taking real-world robotic manipulation and sim-to-real systems beyond demonstrations into sustained operation.
- Strong background in 3D computer vision, kinematics, motion planning (ROS 2, MoveIt 2, cuMotion), and modern machine learning frameworks (PyTorch) with strong C++/Python skills.
- Hands-on experience with robotics simulation and robot-learning frameworks (NVIDIA Isaac Sim/Lab, Newton, MuJoCo, Drake, Gazebo) and sim-to-real validation methods.
- Experience training or fine-tuning vision/multimodal models (VLMs, Cosmos) and optimizing neural networks with TensorRT for edge hardware (Jetson Thor/Orin).
- Proficiency with visual servoing, closed-loop controllers, and hardware API integration (gRPC, SiLA 2, vendor SDKs).
Preferred but not required
- Hands-on experience with OpenUSD scene graphs, custom USD schemas, or NVIDIA Omniverse workflows.
- Experience working with high-throughput lab automation hardware, manipulators, or liquid handlers (e.g., Hamilton, Tecan, Precise Automation, UR arms).
- Background in imitation learning, reinforcement learning, or robot foundation models for contact-rich manipulation and recovery.
- A public portfolio of real-world projects on GitHub/GitLab showcasing work in robotics, simulation, or physical AI.
Relocation benefits are NOT available for this opportunity.
The expected salary range for this position based on the primary location of San Francisco is $168,100 - 312,300 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
See all 742+ Green Card Physical Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Green Card Physical Scientist Jobs.
Get Access To All JobsTips for Finding Green Card Sponsorship as a Physical Scientist
Match your degree to EB-2 requirements
EB-2 covers physical scientists with an advanced degree or a bachelor's degree plus five years of progressive experience in a directly related specialty. Pull your transcripts and performance reviews now so you can document that progression clearly for PERM.
Search verified sponsors on Migrate Mate
Use Migrate Mate to find employers with confirmed green card sponsorship history for physical scientist roles, searchable by city and specialty. Targeting employers who have completed PERM before shortens your path to a qualifying offer.
Prioritize federal research agencies and national labs
Federal contractors and national laboratories operating under DOE, NOAA, or NASA missions regularly sponsor physical scientists through EB-2 and EB-3. These organizations have dedicated HR teams experienced with PERM filings and prevailing wage compliance.
Verify your prevailing wage category before negotiating
Use OFLC Wage Search to check the Level I through Level IV wage for your SOC code and work location before accepting an offer. Your employer's PERM filing must certify wages at or above that level, so underpaying disqualifies the petition.
Understand your O*NET occupation code early
Physical Scientist covers multiple SOC codes on O*NET, including atmospheric, materials, and geosciences specialties. Confirm which code matches your duties before the PERM recruitment phase, since a mismatched code can trigger a DOL audit of the entire filing.
Track your priority date after I-140 approval
Once USCIS approves your I-140, your priority date is locked regardless of a future employer change, as long as the original I-140 remains approved. If per-country backlogs apply to your nationality, monitor the monthly Visa Bulletin to know when your date becomes current.
Green Card Physical Scientist: Frequently Asked Questions
Which green card category fits physical scientist roles, EB-2 or EB-3?
EB-2 applies if your role requires an advanced degree or a bachelor's degree plus five years of progressive experience in a physical science specialty. EB-3 covers physical scientists with a bachelor's degree, and skilled-worker EB-3 applies if you have at least two years of qualifying experience. Your employer selects the category during PERM, so your credentials must meet whichever standard they file under.
How does green card sponsorship differ from H-1B for physical scientists?
H-1B visa grants temporary nonimmigrant status for up to six years and requires an LCA filed with DOL. Green card sponsorship through EB-2 or EB-3 leads to permanent residence and requires PERM labor certification instead, which includes a formal recruitment test and a prevailing wage determination. PERM has no lottery, but per-country annual limits mean nationals of high-demand countries may face priority-date backlogs before they can file I-485.
What does the PERM recruitment test require from an employer sponsoring a physical scientist?
PERM requires your employer to run a DOL-prescribed recruitment campaign, typically including two Sunday newspaper ads, a job order with the state workforce agency, and three additional recruitment steps such as a professional journal posting or career fair. The employer must document that no minimally qualified U.S. worker applied and was rejected. USCIS then reviews the I-140 petition after DOL certifies the PERM application.
How do I find employers who sponsor green cards for physical scientist positions?
Migrate Mate lists employers with verified green card sponsorship history for physical scientist roles, searchable by location and specialty. Targeting employers who have already completed PERM filings in your occupation means less uncertainty about whether a prospective employer understands the process and is willing to fund it.
Can I change employers after my physical scientist green card petition is filed?
AC21 job portability lets you change to a same or similar occupation after your I-485 has been pending for 180 days or more without restarting the green card process. Separately, once your original I-140 is approved, your priority date can carry over to a new employer's I-140 petition. These are two distinct protections, and you need the I-140 approval in hand before the priority date transfers.